The Use of LSA SAF MSG Products for Crop Monitoring

Transkript

1 LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June The Use of LSA SAF MSG Products for Crop Monitoring Anja Klisch,, Antoine Royer, Bettina Baruth European Commision,, JRC, Agriculture Unit, AGRI4CAST Herman Eerens,, Peter Viaene Vlaamse Instelling voor Technologisch Onderzoek Centre for Remote Sensing (VITO-TAP) TAP)

2 JRC-MARS = Agri4Cast + FoodSec LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June CIS CCA WAF IGA ASI MCS SAC AFR

3 T/ha 8,00 7,50 7,00 6,50 6,00 5,50 5,00 4,50 4,00 France - Soft W heat Yield - Area YEAR 2 y = 0,1409x + 4,1052 R= 0, y = -0,0033x + 0,2222x + 3, ,00 R = 0,8998 3, , , , , , , , ha Agri4Cast Infrastructure LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June Pixels Km RS Stations 50/25km grids LAND COVER SOIL+LC LEVEL 0 METEO LEVEL 1 CGMS LEVEL 2 Grids 50/25Km SOIL STAT LEVEL 3 Administrative entities QUANTITATIVE INFO Yield forecast QUALITATIVE INFO Crop monitoring over SPACE and TIME

4 What do we need? LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June Meteorological parameters Temperature TSUM, frost kill Snow cover frost kill protection Radiation crop growth parameters 1) Complement to the station network: Data that serves to enhance our existing meteorological infrastructure, e.g. LST fill gaps, increase the density, perform cross-checking 2) As an alternative to the station network: data that can be used as direct input for CGMS, e.g. radiation, sunshine duration, snow cover perform cross-checking Agrometeorological parameters Evapotranspiration NDVI, fapar, fcover or DMP to derive Phenology or biomass for crop growth model Input/calibration/update of crop growth model

5 OVERVIEW LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June The Use of LSA SAF MSG Products for Crop Monitoring Herman Eerens (VITO) Remote sensing component of the MARS crop monitoring system Adaptation of MSG-data from LSA SAF DMP-estimates from MSG (Dry Matter Productivity) Anja Klisch (JRC-AGRI4CAST) Potential use of MSG Radiation Potential use of MSG Snow Cover CHRONOS information from METOP Conclusions

6 RS: LoRes but High Frequency LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June SPOT-VGT: GLO, 1 km, 1998 NOAA-AVHRR: EUR, 1 km, 1981 TERRA-MODIS: EUR, 250m, 2003 METOP-AVHRR: EUR, 1 km, 2008 MSG EUR(5km) AFR (4km)

7 MSG Processing Chain LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June Snow, Albedo LAI, fapar fcover Land SAF (IM) HDF-5 sftp ENVI-IMG s Daily IMG s Reformat Composit: LST, Radiation, ET Daily Composits: - Radiation, LST, ET - D0 6hUTC D1 6hUTC - LST T min, T mean, T max - Sunshine duration: from cloud mask RAW IMG Projected to Inspire 5km Map: AF4 (Lon/Lat) EU5 (Inspire) Composit Daily IMG s 10-Daily IMG s Extract DATABASE

8 10-daily IMG s for EUROPE (5km) LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June Examples for 2007, June, Dekad 2 Bad filling for LST LST T min, T max, T mean ET since 2007

9 10-Daily IMG s for AFRICA (4km) LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June Radiation Tmean Albedo Examples for 2007, December, Dekad 3 fapar fcover LAI Better filling than Europe No ET and Snow Cover

10 Databases LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June IMGs: DB: EU-Administrative regions (from NUTS2) EU-Main land use classes (CORINE-2000) MSG-image (one per day/dekad) Per Region x Class: MEAN of MSG-values Profile analysis Mean Daily ET in 2007 for Cropland in Italy NUTS2 x K12 = CROPLAND

11 fapar from NOAA-AVHRR MSG LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June NOAA-AVHRR MSG 2007, June, Dek 3 Rostov Mallorca fapar from NOAA & MSG (June 07 May 08): Same trends & order of magnitude (high R² between IMGs of same date) Against MSG: coarse resolution & short time series (compared to AVHRR,VGT) Pro: - Daily instead of 10-daily IMGs -Better filling especially in the north/winter Less missing values -Smoother profile more suited for Chronos analysis

12 Dry Matter Productivity (DMP) LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June MONTEITH-Approach: DMP = R fapar. ε (T min /T max ) Symbol DMP Meaning Dry Matter Productivity R Incoming solar radiation ( µm) 0.48 Fraction of PAR ( µm) in R - fapar /T T min /T max ε Units kgdm/ha/day J/ha/day Source Meteo PAR-fraction absorbed by Vegetation - Remote Sensing Daily min/max temperature C Meteo Efficiency + Autotrophic respiration: kgdm/j - conversion of absorbed PAR-Energy to carbohydrates = f(t 24 ) - maintenance respiration = f(t 12 ) EFFICIENCY [(gdm/j ).(m²/ha)] Myneni & Williams, 1994 fapar vs. NDVI Future: CYCLOPES (INRA) fapar=nn -1 (R RED,R NIR,geom.) Tem perature [ C] Practical use of DMP: Crop monitoring Yield forecasting Carbon sequestration (NPP)

13 LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June JRC-MARS: METEO-sources for DMP GLOBE: Daily data from ERA-40 interpolated to 1 -Grid 1 July 2000: Mean Daily Temperature [ C] 1 July 2000: Solar Irradiation [MJ/m²/day] EUROPE (CGMS) Daily data from ± 4000 Meteo-stations Interpolated to 50km Grid

14 DMP from MSG SPOT-VGT LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June DMP = f(radiation, fapar, T) with common T from ERA-40 fapar + RADIATION DMP SPOT-VGT MSG Example for 2008, February, Dekad 3

15 ? fapar % LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June RAD kj/m²/day DMP-COMPARISON MSG SPOT-VGT DMP kg/ha/day Dekad 3 of February 2008 NS-Transect through Africa along 20 meridian (Lybia Cape Town) Good correspondence!

16 DMP from MSG NOAA-AVHRR LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June ESTONIA PORTUGAL IRAQ TUNESIA MSG AVHRR

17 OVERVIEW LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June The Use of LSA SAF MSG Products for Crop Monitoring Herman Eerens (VITO) Remote sensing component of the MARS crop monitoring system Adaptation of MSG-data from LSA SAF DMP-estimates from MSG (Dry Matter Productivity) Anja Klisch (JRC-AGRI4CAST) Potential use of MSG Radiation Potential use of MSG Snow Cover CHRONOS information from METOP Conclusions

18 Discontinuity of the daily station records LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June Number of daily records per station in Number of daily records % 60% 75% TMAX RAIN RAD % of stations

19 Potential use of MSG radiation LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June Data bases: Time period: Stations reporting radiation (> 50 days) MSG DSSF daily product (composited from 30 min), sunshine duration from LST cloud mask (15 min) Methodology: Regression analysis grouped by the sunshine duration Does the DSSF product fit to station data? Is there a difference in qualtity for algorithms of clear and cloudy sky? MSG Sunshine duration MSG DSSF Station 0: 0 h only clouds 01-96: 15 min 24 h 01-12: 15 min - 3 h 13-24: 3 h 15 min - 6 h 25-36: 6 h 15 min - 9 h 37-48: 9 h 15 min - 12 h Short wave radiation in kj/m2/d 0.3 µm 4.0 µm Linear regression: slope intercept r2 number 6 types per station Global radiation in kj/m2/d 0.3 µm µm

20 Potential use of MSG radiation LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June Data availability 01/01/ /03/ Number of measurements in d % 65 % Stations and MSG DSSF Stations MSG DSSF Number of stations in %

21 Potential use of MSG radiation LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June Regression analysis of radiation between CGMS station data and MSG DSSF data 01/01/ /03/2008 Sunshine 0 h Sunshine 15 min - 24 h Data sources: LSA SAF, MSG DSSF and LST products / station data from MARS database / administrative boundaries from EuroGeographics

22 Potential use of MSG radiation LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June Sunshine 15 min - 3 h Sunshine 3 h 15 min - 6 h Sunshine 6 h 15 min - 9 h Sunshine 9 h 15 min - 12 h Data sources: LSA SAF, MSG DSSF and LST products / station data from MARS database / administrative boundaries from EuroGeographics

23 Potential use of MSG radiation LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June Percentage difference of radiation between MSG DSSF data CGMS and station data 01/06/ /06/ /12/ /12/2007 Relative difference between MSG and station data in % % 2 % % 14 % Number of measurements in % Relative difference between MSG and station data in % Number of measurements in % Total number of stations/days: 7404 Total number of stations/days: 7020 Relative difference = (DSSF Station) / Station * 100

24 Potential use of MSG radiation LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June Preliminary results Good relationship between MSG and station (majority r2 > 0.8) Spatial variation: - no difference for different sunshine durations visible no quality difference between clear and cloudy sky? - impact of number of measurements and station outliers Temporal variation: between seasons (high/low radiation) Station outliers equally distributed Quality control of stations necessary! MSG DSSF can be used for cross-checking Comparison with modelled radiation in CGMS

25 Potential use of MSG snow LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June Data bases: Time periods: / Stations reporting snow depth MSG SC daily product Methodology: If snow is present, how good is MSG capable to detect it? analysis of snow presence/absence in MSG SC data compared to stations Stations MSG SC Excluded from analysis No value % Agreements % Disagreements 0, 251, 254, 4: No value Compared cases 0: no snow > 0: snow 3: no snow 1, 2: snow (full and partial)

26 Potential use of MSG snow LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June Data availability 01/10/ /04/2008 Number of measurements in d Stations and MSG SC Stations MSG SC Number of stations in %

27 Potential use of MSG snow LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June Comparison of snow presence in CGMS station data and MSG SC data 01/10/ /04/2008 Disagreement Agreement Agreement / disagreement in both datasets in percent Data sources: LSA SAF, MSG SC product / station data from MARS database / administrative boundaries from EuroGeographics

28 Potential use of MSG snow LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June Comparison of snow presence in CGMS station data and MSG-SEVIRI data Agreements in both datasets 01/01/ /03/ /01/ /03/2008 Agreement in both datasets in percent Data sources: LSA SAF, MSG SC product / station data from MARS database / administrative boundaries from EuroGeographics

29 Potential use of MSG snow LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June MSG Snow cover classes 1 (full): 55 % 2 (partial): 1 % 3 (no): 44 % Snow depth in cm Number of measurements in % Total number of stations/days: Stations with snow depth > 0 cm and measurements for MSG

30 Conclusion on snow LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June Number of stations with agreements slightly higher than the one with disagreements Most cases: both snow and station snow, MSG no snow underestimation of snow (restrictive) Compared to 2006 analysis slightly smaller percentage for agreements less snow days in central Europe Analysis of relation between snow depth and MSG snow cover classes only little part of partial snow cover relative high snow depth for no snow cover Difficult to get continuous frost kill monitoring

31 CHRONOS information from METOP LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June NDVI Oct SlopeUp DECADES MaxDec SlopeDown Sept MaxVal HalfSenVal StartVal EndVal Chronos phasing indicators: StartDec MaxDec HalfSenDec EndVal Chronos intensity indicators: StartVal MaxVal HalfSenVal EndVal Chronos duration indicators: Length LengthUp LengthDown StartDec Length LengthUp EndDec HalfSenDec LengthDown Chronos slope indicators: SlopeUp SlopeDown

32 CHRONOS information from METOP LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June ,0 0,8 Rice in Italy (Piemonte) MaxDec: 22 MaxVal: 0,88 NDVI 0,6 0,4 HalfDec: 27 HalfVal: 0,51 0,2 0,0 Oct StartDec: 11 StartVal: 0,17 Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Decades Data sources: MARS database, SPOT-VEGETATION, NDVI, 10 daily / administrative boundaries from EuroGeographics

33 CHRONOS information from METOP LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June DOP = day of planting DOE = day of emergence DOA = day of anthesis DOH = day of harvest * start, max, end half senescence derived fro NDVI Data sources: MARS database, SPOT-VEGETATION, NDVI, 10 daily / administrative boundaries from EuroGeographics

34 Conclusion LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June Meteo parameters Temperature: analysis 2005, encouraging results for linear regression between MSG LST and average temperature, strong limitation spatial and temporal coverage Radiation: promising, strange outliers to be analysed Snow cover: too restrictive identification for continuous frost kill monitoring How do we spatialise the calibration? Agrometeo parameters: Analysis of Chronos Key Indicators based on NDVI, fapar, derived from METOP-AVHRR and NOAA-AVHRR for assimilation in CGMS level 2 and 3 Qualitative analysis fo vegetation parameters of METOP-AVHRR combined with NOAA- AVHRR for scenario in CGMS level 3 (e.g. most similar year) BUT: pre-condition homogeneous historical dataset General comments Back-processing: homogeneous datasets for comparison and stable relation over space and time Good collaboration and operational services with LSA SAF: clear product description, versioning

35 LSA SAF3rd User Training Workshop, IM, Lisbon, 4 6 June Thanks for your attention Anja Klisch,, Antoine Royer, Bettina Baruth European Commision,, JRC, Agriculture Unit, AGRI4CAST Herman Eerens, Peter Viaene Vlaamse Instelling voor Technologisch Onderzoek Centre for Remote Sensing (VITO-TAP TAP)